## This file generates direct effect estimates
## If RDS files for the models already exist
## Go to the file "present_results.R" to avoid re-running
## as code takes a while to generate the MI estimates.
## Code to combine data over several impuations and properly pools the results,
## Since the randomization probability is constant, we can do this with gee
num_impute = 20
for(impute_iter in 1:20){
print(impute_iter)
analysis_dat_gee = IHS_MRT[[impute_iter]]
# MOOD
analysis_dat_gee$week_category_new = ifelse(analysis_dat_gee$week_category == "mood",1,0)
p_tilde = mean(analysis_dat_gee$week_category_new)
analysis_dat_gee$weights = ifelse(analysis_dat_gee$week_category_new==1,p_tilde/(1/4),
(1-p_tilde)/(3/4))
analysis_dat_gee$week_category_new_c = analysis_dat_gee$week_category_new - p_tilde
centering_par = aggregate(MOODprev~study_week, data = analysis_dat_gee, mean)
colnames(centering_par)[2] = "centering_par"
analysis_dat_gee = merge(analysis_dat_gee, centering_par, by = "study_week")
analysis_dat_gee$MOODprev_c = analysis_dat_gee$MOODprev - analysis_dat_gee$centering_par
gee_result_mood = geeglm(MOOD ~ week_category_new_c , data = analysis_dat_gee,weights = weights, id = UserID, scale.fix = T)
gee_result_mood2 = geeglm(MOOD ~ week_category_new_c + MOODprev_c , data = analysis_dat_gee, weights = weights, id = UserID, scale.fix = T)
gee_result_mood3 = geeglm(MOOD ~ week_category_new_c * MOODprev_c, data = analysis_dat_gee, weights = weights, id = UserID, scale.fix = T)
# gee_result_mood4 = geeglm(MOOD ~ week_category_new_c * (MOODprev_c+ Neu0_c + pre_intern_mood_c), data = analysis_dat_gee, weights = weights, id = UserID, scale.fix = T)
mood = data.frame(x = c("I","II", "III"),
y = c(coef(gee_result_mood)["week_category_new_c"],
coef(gee_result_mood2)["week_category_new_c"],
coef(gee_result_mood3)["week_category_new_c"]),
se = c(summary(gee_result_mood)$coefficients["week_category_new_c","Std.err"],
summary(gee_result_mood2)$coefficients["week_category_new_c","Std.err"],
summary(gee_result_mood3)$coefficients["week_category_new_c","Std.err"]),
p = c(summary(gee_result_mood)$coefficients["week_category_new_c","Pr(>|W|)"],
summary(gee_result_mood2)$coefficients["week_category_new_c","Pr(>|W|)"],
summary(gee_result_mood3)$coefficients["week_category_new_c","Pr(>|W|)"]))
# STEP
analysis_dat_gee = IHS_MRT[[impute_iter]]
analysis_dat_gee$week_category_new = ifelse(analysis_dat_gee$week_category == "activity",1,0)
p_tilde = mean(analysis_dat_gee$week_category_new)
analysis_dat_gee$weights = ifelse(analysis_dat_gee$week_category_new==1,p_tilde/(1/4),
(1-p_tilde)/(3/4))
analysis_dat_gee$week_category_new_c = analysis_dat_gee$week_category_new - p_tilde
centering_par = aggregate(STEP_COUNTprev~study_week, data = analysis_dat_gee, mean)
colnames(centering_par)[2] = "centering_par"
analysis_dat_gee = merge(analysis_dat_gee, centering_par, by = "study_week")
analysis_dat_gee$STEP_COUNTprev_c = analysis_dat_gee$STEP_COUNTprev - analysis_dat_gee$centering_par
gee_result = geeglm(STEP_COUNT ~ week_category_new_c , data = analysis_dat_gee,weights = weights, id = UserID, scale.fix = T)
gee_result2 = geeglm(STEP_COUNT ~ week_category_new_c + STEP_COUNTprev_c, data = analysis_dat_gee, weights = weights, id = UserID, scale.fix = T)
gee_result3 = geeglm(STEP_COUNT ~ week_category_new_c * STEP_COUNTprev_c, data = analysis_dat_gee, weights = weights, id = UserID, scale.fix = T)
step = data.frame(x = c("I","II", "III"),
y = c(coef(gee_result)["week_category_new_c"],
coef(gee_result2)["week_category_new_c"],
coef(gee_result3)["week_category_new_c"]),
se = c(summary(gee_result)$coefficients["week_category_new_c","Std.err"],
summary(gee_result2)$coefficients["week_category_new_c","Std.err"],
summary(gee_result3)$coefficients["week_category_new_c","Std.err"]),
p = c(summary(gee_result)$coefficients["week_category_new_c","Pr(>|W|)"],
summary(gee_result2)$coefficients["week_category_new_c","Pr(>|W|)"],
summary(gee_result3)$coefficients["week_category_new_c","Pr(>|W|)"]))
df = rbind(mood, step)
df$outcome = rep(c("MOOD","STEP"),each = 3)
if(mood[3,3]<mood[1,3] & step[3,3]<step[1,3]){
print(df)
print((mood[1,3]^2)/(mood[3,3]^2))
print((step[1,3]^2)/(step[3,3]^2))
print((mood[1,3]^2)/(mood[2,3]^2))
print((step[1,3]^2)/(step[2,3]^2))
p_1 = ggplot(df, aes(x= x,y = y)) +
geom_pointrange(
aes(ymin = y-1.96* se, ymax = y+1.96*se, color = outcome ),
position = position_dodge(0.3)
)+
# scale_color_manual(values = c("#00AFBB", "#E7B800"))+
scale_color_brewer(palette="Paired")+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())+
xlab("Model")+
ylab("Causal Effect")+
geom_hline(yintercept=0, linetype="dashed")
p_2 = ggplot(data=df, aes(x=x, y=se,fill = outcome)) +
geom_bar(stat="identity", position=position_dodge())+
scale_fill_brewer(palette="Paired")+
# scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())+
xlab("Model")+
ylab("Standard Error")
p = ggarrange(p_1, p_2, ncol=2,legend = "bottom",common.legend = T)
print(p)
}else{
print("no improvement")
}
}
## [1] 1
## x y se p outcome
## 1 I -0.01872703 0.013595793 0.16838435 MOOD
## 2 II -0.01977342 0.008954228 0.02722494 MOOD
## 3 III -0.01975578 0.008952381 0.02733064 MOOD
## 4 I 0.02742969 0.032505287 0.39875145 STEP
## 5 II 0.03645874 0.025868008 0.15871272 STEP
## 6 III 0.03643723 0.025853666 0.15872756 STEP
## [1] 2.306386
## [1] 1.580752
## [1] 2.305434
## [1] 1.579
## [1] 2
## x y se p outcome
## 1 I -0.01658092 0.013718963 0.22681153 MOOD
## 2 II -0.01653546 0.009003350 0.06627045 MOOD
## 3 III -0.01653652 0.008997292 0.06607065 MOOD
## 4 I 0.04986343 0.031569970 0.11423114 STEP
## 5 II 0.06438468 0.025335928 0.01104600 STEP
## 6 III 0.06426280 0.025309918 0.01111583 STEP
## [1] 2.324979
## [1] 1.555847
## [1] 2.32185
## [1] 1.552654
## [1] 3
## x y se p outcome
## 1 I 1.207777e-05 0.013585127 0.99929065 MOOD
## 2 II -8.040910e-04 0.008863267 0.92771369 MOOD
## 3 III -7.954073e-04 0.008862934 0.92848956 MOOD
## 4 I 3.916536e-02 0.031442450 0.21290380 STEP
## 5 II 6.148945e-02 0.025311849 0.01512905 STEP
## 6 III 6.119130e-02 0.025271276 0.01546181 STEP
## [1] 2.349483
## [1] 1.548027
## [1] 2.349307
## [1] 1.543068
## [1] 4
## x y se p outcome
## 1 I -0.011294787 0.013528373 0.4037762 MOOD
## 2 II -0.008254812 0.008794006 0.3478919 MOOD
## 3 III -0.008242203 0.008790410 0.3484316 MOOD
## 4 I -0.002308783 0.032325676 0.9430614 STEP
## 5 II -0.004649533 0.025782661 0.8568891 STEP
## 6 III -0.004651356 0.025786586 0.8568551 STEP
## [1] 2.368497
## [1] 1.571475
## [1] 2.36656
## [1] 1.571954
## [1] 5
## x y se p outcome
## 1 I -0.01762813 0.013692145 0.19793294 MOOD
## 2 II -0.01966000 0.008916741 0.02746521 MOOD
## 3 III -0.01964212 0.008917972 0.02762776 MOOD
## 4 I 0.01738541 0.032503912 0.59273887 STEP
## 5 II 0.04064054 0.025929531 0.11703386 STEP
## 6 III 0.04052022 0.025885633 0.11749960 STEP
## [1] 2.357278
## [1] 1.576717
## [1] 2.357929
## [1] 1.571383
## [1] 6
## x y se p outcome
## 1 I 0.0005146822 0.013524927 0.96964436 MOOD
## 2 II -0.0052436948 0.008853257 0.55365645 MOOD
## 3 III -0.0051698826 0.008857654 0.55944726 MOOD
## 4 I 0.0358157433 0.032140695 0.26513227 STEP
## 5 II 0.0465940096 0.025677222 0.06958467 STEP
## 6 III 0.0465863683 0.025659366 0.06943640 STEP
## [1] 2.331484
## [1] 1.568985
## [1] 2.333801
## [1] 1.566803
## [1] 7
## x y se p outcome
## 1 I -0.005670894 0.013603765 0.67677926 MOOD
## 2 II -0.018616006 0.008889991 0.03625633 MOOD
## 3 III -0.018543945 0.008903818 0.03727895 MOOD
## 4 I -0.010791606 0.031671356 0.73330151 STEP
## 5 II 0.002151790 0.025417863 0.93253440 STEP
## 6 III 0.002012075 0.025392684 0.93684301 STEP
## [1] 2.334349
## [1] 1.555665
## [1] 2.341615
## [1] 1.552585
## [1] 8
## x y se p outcome
## 1 I -0.02762830 0.013670653 0.0432804084 MOOD
## 2 II -0.03272922 0.009043580 0.0002956801 MOOD
## 3 III -0.03269616 0.009048772 0.0003022947 MOOD
## 4 I 0.03492059 0.031961718 0.2745802858 STEP
## 5 II 0.07513584 0.025481228 0.0031914013 STEP
## 6 III 0.07511316 0.025413484 0.0031201920 STEP
## [1] 2.282439
## [1] 1.581728
## [1] 2.285061
## [1] 1.573329
## [1] 9
## x y se p outcome
## 1 I -0.01828157 0.013647248 0.180382297 MOOD
## 2 II -0.01429081 0.008953156 0.110449472 MOOD
## 3 III -0.01438543 0.008942441 0.107688595 MOOD
## 4 I 0.05135366 0.032175825 0.110481527 STEP
## 5 II 0.07264397 0.025633176 0.004597188 STEP
## 6 III 0.07244523 0.025594398 0.004647366 STEP
## [1] 2.329046
## [1] 1.580409
## [1] 2.323475
## [1] 1.575631
## [1] 10
## x y se p outcome
## 1 I -0.014681983 0.013621473 0.28109813 MOOD
## 2 II -0.016490215 0.008910379 0.06421635 MOOD
## 3 III -0.016474358 0.008911498 0.06450646 MOOD
## 4 I 0.008592373 0.032053616 0.78865121 STEP
## 5 II 0.029220235 0.025785482 0.25712820 STEP
## 6 III 0.028983070 0.025746084 0.26028095 STEP
## [1] 2.336398
## [1] 1.55
## [1] 2.336984
## [1] 1.545267
## [1] 11
## x y se p outcome
## 1 I 0.004563991 0.013447188 0.73430756 MOOD
## 2 II 0.001822605 0.008811816 0.83613759 MOOD
## 3 III 0.001866402 0.008812025 0.83226176 MOOD
## 4 I 0.056382735 0.031868716 0.07685735 STEP
## 5 II 0.060594889 0.025340154 0.01679067 STEP
## 6 III 0.060604009 0.025332931 0.01674308 STEP
## [1] 2.328689
## [1] 1.582553
## [1] 2.3288
## [1] 1.581651
## [1] 12
## x y se p outcome
## 1 I -0.003860798 0.013561669 0.7758858 MOOD
## 2 II -0.004581749 0.008948823 0.6086548 MOOD
## 3 III -0.004573066 0.008948055 0.6093033 MOOD
## 4 I 0.024570514 0.032053850 0.4433559 STEP
## 5 II 0.040924905 0.025636471 0.1104096 STEP
## 6 III 0.040688475 0.025604958 0.1120410 STEP
## [1] 2.297042
## [1] 1.567156
## [1] 2.296648
## [1] 1.563306
## [1] 13
## x y se p outcome
## 1 I -0.02337675 0.013680626 0.08749748 MOOD
## 2 II -0.02269269 0.008984841 0.01154797 MOOD
## 3 III -0.02270080 0.008982595 0.01149764 MOOD
## 4 I 0.01303619 0.031796944 0.68181870 STEP
## 5 II 0.03324951 0.025317105 0.18907450 STEP
## 6 III 0.03321663 0.025281699 0.18889309 STEP
## [1] 2.319574
## [1] 1.581824
## [1] 2.318414
## [1] 1.577403
## [1] 14
## x y se p outcome
## 1 I -0.010331549 0.013468486 0.4430276472 MOOD
## 2 II -0.009086433 0.008877653 0.3060632413 MOOD
## 3 III -0.009113562 0.008871426 0.3042820423 MOOD
## 4 I 0.063585768 0.031735225 0.0451093088 STEP
## 5 II 0.088794330 0.025461650 0.0004877863 STEP
## 6 III 0.088374094 0.025414636 0.0005065070 STEP
## [1] 2.304893
## [1] 1.559249
## [1] 2.30166
## [1] 1.553496
## [1] 15
## x y se p outcome
## 1 I -0.009554384 0.013458239 0.47774862 MOOD
## 2 II -0.006608033 0.008834430 0.45446841 MOOD
## 3 III -0.006649780 0.008829303 0.45136047 MOOD
## 4 I 0.027586314 0.031835281 0.38619807 STEP
## 5 II 0.042480173 0.025504875 0.09579895 STEP
## 6 III 0.042462539 0.025478717 0.09559624 STEP
## [1] 2.323398
## [1] 1.561213
## [1] 2.320702
## [1] 1.558013
## [1] 16
## x y se p outcome
## 1 I -0.005238215 0.013582496 0.6997490 MOOD
## 2 II -0.001272447 0.008955941 0.8870180 MOOD
## 3 III -0.001328008 0.008948957 0.8820286 MOOD
## 4 I 0.017113866 0.031849241 0.5910329 STEP
## 5 II 0.028641637 0.025738487 0.2657969 STEP
## 6 III 0.028632696 0.025717473 0.2655557 STEP
## [1] 2.303639
## [1] 1.533704
## [1] 2.300047
## [1] 1.531201
## [1] 17
## x y se p outcome
## 1 I -0.0012827988 0.013471757 0.92413902 MOOD
## 2 II 0.0003717823 0.008993679 0.96702630 MOOD
## 3 III 0.0003493707 0.008990216 0.96900104 MOOD
## 4 I 0.0274970792 0.031558328 0.38358505 STEP
## 5 II 0.0547885480 0.025292741 0.03029768 STEP
## 6 III 0.0545019154 0.025246001 0.03086308 STEP
## [1] 2.245475
## [1] 1.562582
## [1] 2.243746
## [1] 1.556812
## [1] 18
## x y se p outcome
## 1 I -0.0008638969 0.013602730 0.94936113 MOOD
## 2 II -0.0053191771 0.008869230 0.54868363 MOOD
## 3 III -0.0052490939 0.008871499 0.55406447 MOOD
## 4 I 0.0127488432 0.031645205 0.68704527 STEP
## 5 II 0.0545816095 0.025531055 0.03252878 STEP
## 6 III 0.0542183224 0.025462675 0.03322734 STEP
## [1] 2.35103
## [1] 1.544571
## [1] 2.352233
## [1] 1.536308
## [1] 19
## x y se p outcome
## 1 I -0.02177823 0.013598100 1.092520e-01 MOOD
## 2 II -0.02462846 0.008803784 5.150200e-03 MOOD
## 3 III -0.02461238 0.008806523 5.193351e-03 MOOD
## 4 I 0.08187106 0.031528759 9.412120e-03 STEP
## 5 II 0.09869335 0.025306845 9.624524e-05 STEP
## 6 III 0.09840035 0.025273136 9.881794e-05 STEP
## [1] 2.384226
## [1] 1.556308
## [1] 2.38571
## [1] 1.552165
## [1] 20
## x y se p outcome
## 1 I 0.003870295 0.013548462 7.751362e-01 MOOD
## 2 II 0.005923975 0.008867823 5.041142e-01 MOOD
## 3 III 0.005893297 0.008863239 5.061051e-01 MOOD
## 4 I 0.078203219 0.031585835 1.329026e-02 STEP
## 5 II 0.098621643 0.025342655 9.961505e-05 STEP
## 6 III 0.098267383 0.025303353 1.029332e-04 STEP
## [1] 2.336657
## [1] 1.558219
## [1] 2.334243
## [1] 1.55339
Plot
B spline exploration
# library(splines)
#### MOOD
for (i in 1:20){
print(i)
analysis_dat_gee = IHS_MRT[[i]]
analysis_dat_gee$week_category_new = ifelse(analysis_dat_gee$week_category == "mood",1,0)
p_tilde = mean(analysis_dat_gee$week_category_new)
analysis_dat_gee$weights = ifelse(analysis_dat_gee$week_category_new==1,p_tilde/(1/4),
(1-p_tilde)/(3/4))
analysis_dat_gee$week_category_new_c = analysis_dat_gee$week_category_new - p_tilde
centering_par = aggregate(MOODprev~study_week, data = analysis_dat_gee, mean)
colnames(centering_par)[2] = "centering_par"
analysis_dat_gee = merge(analysis_dat_gee, centering_par, by = "study_week")
analysis_dat_gee$MOODprev_c = analysis_dat_gee$MOODprev - analysis_dat_gee$centering_par
### models
fit_wcls = geeglm(MOOD ~ week_category_new_c * study_week, data = analysis_dat_gee,weights = weights, id = UserID, scale.fix = T)
# print(summary(fit_wcls))
fit_cwcls = geeglm(MOOD ~ week_category_new_c * (study_week + MOODprev_c), weights = weights, data = analysis_dat_gee, id = UserID, scale.fix = T)
# print(summary(fit_cwcls))
p1= plot_moderator(fit_wcls, fit_cwcls, moderator= "study_week", x_name = "Study Week")
### Step
analysis_dat_gee = IHS_MRT[[i]]
analysis_dat_gee$week_category_new = ifelse(analysis_dat_gee$week_category == "activity",1,0)
p_tilde = mean(analysis_dat_gee$week_category_new)
analysis_dat_gee$weights = ifelse(analysis_dat_gee$week_category_new==1,p_tilde/(1/4),
(1-p_tilde)/(3/4))
analysis_dat_gee$week_category_new_c = analysis_dat_gee$week_category_new - p_tilde
centering_par = aggregate(STEP_COUNTprev~study_week, data = analysis_dat_gee, mean)
colnames(centering_par)[2] = "centering_par"
analysis_dat_gee = merge(analysis_dat_gee, centering_par, by = "study_week")
analysis_dat_gee$STEP_COUNTprev_c = analysis_dat_gee$STEP_COUNTprev - analysis_dat_gee$centering_par
fit_wcls = geeglm(STEP_COUNT ~ week_category_new_c * study_week, data = analysis_dat_gee,weights = weights, id = UserID, scale.fix = T)
# print(summary(fit_wcls))
fit_cwcls = geeglm(STEP_COUNT ~ week_category_new_c * (study_week + STEP_COUNTprev_c),weights = weights, data = analysis_dat_gee, id = UserID, scale.fix = T)
# print(summary(fit_cwcls))
p2= plot_moderator(fit_wcls, fit_cwcls, moderator= "study_week", y_name = "Step Count Change", x_name = "Study Week")
p = ggarrange(p1, p2, ncol=2,legend = "bottom",common.legend = T)
print(p)
}
## [1] 1
## [1] 2.342294 2.352784 2.364056 2.375957 2.388162 2.400082 2.410745 2.418698
## [9] 2.421987 2.418374 2.405918 2.383857 2.353366 2.317554 2.280477 2.245770
## [17] 2.215752 2.191310 2.172293 2.158004 2.147569 2.140150 2.135030 2.131634
## [25] 2.129518 2.128344
## [1] 1.653231 1.657029 1.660742 1.664175 1.667036 1.668895 1.669159 1.667053
## [9] 1.661674 1.652139 1.637886 1.619062 1.596803 1.573135 1.550418 1.530611
## [17] 1.514805 1.503196 1.495366 1.490614 1.488203 1.487482 1.487933 1.489166
## [25] 1.490899 1.492931
## [1] 2
## [1] 2.369185 2.371893 2.374802 2.377872 2.381019 2.384094 2.386852 2.388926
## [9] 2.389828 2.389003 2.385969 2.380529 2.372961 2.364016 2.354685 2.345874
## [17] 2.338178 2.331847 2.326864 2.323075 2.320269 2.318241 2.316813 2.315838
## [25] 2.315203 2.314820
## [1] 1.599698 1.602128 1.604522 1.606765 1.608682 1.610018 1.610413 1.609392
## [9] 1.606390 1.600833 1.592313 1.580831 1.567000 1.552028 1.537410 1.524456
## [17] 1.513959 1.506131 1.500757 1.497415 1.495633 1.494989 1.495140 1.495828
## [25] 1.496861 1.498105
## [1] 3
## [1] 2.373318 2.379610 2.386469 2.393848 2.401619 2.409513 2.417056 2.423499
## [9] 2.427779 2.428589 2.424655 2.415211 2.400490 2.381884 2.361535 2.341597
## [17] 2.323634 2.308435 2.296146 2.286529 2.279177 2.273659 2.269579 2.266609
## [25] 2.264487 2.263008
## [1] 1.575503 1.579669 1.584006 1.588411 1.592705 1.596600 1.599660 1.601264
## [9] 1.600605 1.596765 1.588933 1.576746 1.560649 1.541998 1.522731 1.504740
## [17] 1.489339 1.477081 1.467910 1.461425 1.457099 1.454423 1.452965 1.452382
## [25] 1.452415 1.452873
## [1] 4
## [1] 2.365745 2.370371 2.375520 2.381206 2.387400 2.393989 2.400733 2.407197
## [9] 2.412698 2.416311 2.416996 2.413903 2.406765 2.396152 2.383337 2.369841
## [17] 2.356951 2.345470 2.335735 2.327747 2.321334 2.316256 2.312269 2.309154
## [25] 2.306729 2.304846
## [1] 1.639646 1.644528 1.649439 1.654176 1.658427 1.661716 1.663369 1.662485
## [9] 1.657988 1.648801 1.634201 1.614277 1.590267 1.564429 1.539392 1.517331
## [17] 1.499460 1.486026 1.476615 1.470512 1.466956 1.465272 1.464917 1.465477
## [25] 1.466648 1.468212
## [1] 5
## [1] 2.413985 2.421676 2.429860 2.438387 2.446965 2.455091 2.461967 2.466434
## [9] 2.466994 2.462016 2.450220 2.431345 2.406634 2.378682 2.350589 2.324942
## [17] 2.303249 2.285956 2.272787 2.263124 2.256265 2.251566 2.248489 2.246611
## [25] 2.245608 2.245240
## [1] 1.643200 1.649827 1.656648 1.663444 1.669850 1.675295 1.678938 1.679628
## [9] 1.675957 1.666495 1.650256 1.627319 1.599239 1.568833 1.539302 1.513216
## [17] 1.491950 1.475752 1.464128 1.456269 1.451321 1.448535 1.447307 1.447175
## [25] 1.447800 1.448933
## [1] 6
## [1] 2.350824 2.357008 2.363747 2.370992 2.378605 2.386305 2.393598 2.399706
## [9] 2.403531 2.403748 2.399103 2.388919 2.373592 2.354677 2.334398 2.314877
## [17] 2.297566 2.283122 2.271589 2.262667 2.255923 2.250918 2.247264 2.244644
## [25] 2.242808 2.241562
## [1] 1.621051 1.625414 1.629858 1.634228 1.638270 1.641596 1.643639 1.643635
## [9] 1.640649 1.633723 1.622174 1.605998 1.586177 1.564594 1.543483 1.524718
## [17] 1.509369 1.497689 1.489363 1.483817 1.480425 1.478630 1.477981 1.478130
## [25] 1.478822 1.479873
## [1] 7
## [1] 2.350725 2.361797 2.373826 2.386706 2.400163 2.413657 2.426253 2.436486
## [9] 2.442311 2.441288 2.431158 2.410811 2.381189 2.345382 2.307632 2.271845
## [17] 2.240577 2.214871 2.194659 2.179276 2.167856 2.159557 2.153654 2.149560
## [25] 2.146821 2.145090
## [1] 1.614722 1.617037 1.619234 1.621170 1.622631 1.623313 1.622802 1.620567
## [9] 1.616002 1.608532 1.597825 1.584054 1.568078 1.551359 1.535558 1.522022
## [17] 1.511461 1.503948 1.499128 1.496465 1.495410 1.495490 1.496335 1.497671
## [25] 1.499303 1.501096
## [1] 8
## [1] 2.315499 2.321921 2.328843 2.336183 2.343759 2.351232 2.358036 2.363310
## [9] 2.365882 2.364378 2.357561 2.344861 2.326864 2.305368 2.282822 2.261495
## [17] 2.242885 2.227616 2.215649 2.206592 2.199925 2.195137 2.191790 2.189528
## [25] 2.188074 2.187219
## [1] 1.626307 1.630751 1.635303 1.639819 1.644060 1.647657 1.650069 1.650552
## [9] 1.648181 1.641972 1.631159 1.615600 1.596113 1.574472 1.552918 1.533439
## [17] 1.517262 1.504772 1.495733 1.489599 1.485745 1.483597 1.482683 1.482636
## [25] 1.483185 1.484131
## [1] 9
## [1] 2.304705 2.316168 2.328837 2.342696 2.357597 2.373151 2.388600 2.402649
## [9] 2.413359 2.418223 2.414640 2.400839 2.376923 2.345272 2.309812 2.274578
## [17] 2.242537 2.215206 2.192916 2.175286 2.161628 2.151195 2.143309 2.137402
## [25] 2.133016 2.129797
## [1] 1.625720 1.629198 1.632729 1.636184 1.639350 1.641905 1.643380 1.643143
## [9] 1.640428 1.634461 1.624714 1.611241 1.594913 1.577323 1.560305 1.545346
## [17] 1.533255 1.524172 1.517795 1.513633 1.511169 1.509953 1.509621 1.509900
## [25] 1.510589 1.511545
## [1] 10
## [1] 2.365099 2.372782 2.381073 2.389876 2.398977 2.407972 2.416184 2.422579
## [9] 2.425739 2.423997 2.415844 2.400577 2.378909 2.353026 2.325911 2.300311
## [17] 2.278028 2.259793 2.245544 2.234791 2.226898 2.221251 2.217319 2.214675
## [25] 2.212989 2.212010
## [1] 1.613698 1.618310 1.622950 1.627433 1.631467 1.634612 1.636240 1.635508
## [9] 1.631403 1.622888 1.609234 1.590453 1.567637 1.542889 1.518730 1.497306
## [17] 1.479863 1.466701 1.457458 1.451454 1.447954 1.446295 1.445944 1.446494
## [25] 1.447644 1.449180
## [1] 11
## [1] 2.340565 2.349804 2.359852 2.370632 2.381936 2.393349 2.404140 2.413158
## [9] 2.418777 2.419010 2.411942 2.396477 2.373102 2.344098 2.312849 2.282661
## [17] 2.255848 2.233489 2.215682 2.201969 2.191670 2.184091 2.178620 2.174755
## [25] 2.172101 2.170355
## [1] 1.640925 1.645415 1.649948 1.654352 1.658353 1.661542 1.663327 1.662917
## [9] 1.659352 1.651643 1.639068 1.621584 1.600152 1.576701 1.553596 1.532906
## [17] 1.515877 1.502870 1.493596 1.487446 1.483733 1.481831 1.481230 1.481532
## [25] 1.482446 1.483761
## [1] 12
## [1] 2.304858 2.311519 2.318826 2.326747 2.335169 2.343834 2.352268 2.359699
## [9] 2.365002 2.366768 2.363576 2.354520 2.339759 2.320715 2.299648 2.278861
## [17] 2.260041 2.244046 2.231053 2.220829 2.212960 2.207000 2.202545 2.199254
## [25] 2.196856 2.195139
## [1] 1.614024 1.618285 1.622637 1.626932 1.630929 1.634257 1.636375 1.636545
## [9] 1.633866 1.627404 1.616480 1.601063 1.582070 1.561295 1.540891 1.522681
## [17] 1.507726 1.496292 1.488095 1.482591 1.479181 1.477329 1.476599 1.476654
## [25] 1.477245 1.478193
## [1] 13
## [1] 2.311661 2.318372 2.325830 2.334049 2.342969 2.352405 2.361970 2.370980
## [9] 2.378380 2.382768 2.382620 2.376781 2.365058 2.348522 2.329211 2.309389
## [17] 2.290849 2.274628 2.261079 2.250110 2.241409 2.234598 2.229309 2.225226
## [25] 2.222087 2.219684
## [1] 1.593297 1.597570 1.602097 1.606806 1.611555 1.616105 1.620076 1.622911
## [9] 1.623865 1.622061 1.616673 1.607241 1.594005 1.578038 1.560993 1.544575
## [17] 1.530048 1.518046 1.508656 1.501628 1.496566 1.493059 1.490741 1.489309
## [25] 1.488529 1.488221
## [1] 14
## [1] 2.392129 2.402665 2.413798 2.425293 2.436710 2.447312 2.455944 2.460950
## [9] 2.460208 2.451428 2.432847 2.404190 2.367364 2.326226 2.285325 2.248409
## [17] 2.217593 2.193411 2.175353 2.162427 2.153553 2.147753 2.144229 2.142359
## [25] 2.141675 2.141831
## [1] 1.593111 1.596206 1.599384 1.602545 1.605521 1.608049 1.609745 1.610079
## [9] 1.608398 1.604026 1.596465 1.585689 1.572357 1.557753 1.543412 1.530618
## [17] 1.520107 1.512058 1.506266 1.502347 1.499886 1.498510 1.497917 1.497875
## [25] 1.498210 1.498800
## [1] 15
## [1] 2.372842 2.382123 2.392066 2.402529 2.413222 2.423619 2.432861 2.439655
## [9] 2.442255 2.438636 2.426985 2.406510 2.378176 2.344787 2.310128 2.277673
## [17] 2.249677 2.227015 2.209546 2.196586 2.187284 2.180822 2.176507 2.173785
## [25] 2.172231 2.171527
## [1] 1.568158 1.572473 1.577071 1.581889 1.586797 1.591570 1.595845 1.599074
## [9] 1.600511 1.599257 1.594435 1.585509 1.572642 1.556864 1.539837 1.523313
## [17] 1.508617 1.496429 1.486860 1.479670 1.474466 1.470834 1.468405 1.466874
## [25] 1.466005 1.465617
## [1] 16
## [1] 2.305496 2.312883 2.320989 2.329779 2.339123 2.348733 2.358074 2.366277
## [9] 2.372078 2.373895 2.370152 2.359866 2.343265 2.321987 2.298580 2.275595
## [17] 2.254871 2.237321 2.223108 2.211951 2.203383 2.196908 2.192077 2.188516
## [25] 2.185928 2.184081
## [1] 1.577435 1.581546 1.585754 1.589923 1.593831 1.597135 1.599336 1.599749
## [9] 1.597526 1.591769 1.581790 1.567469 1.549564 1.529695 1.509901 1.491991
## [17] 1.477086 1.465543 1.457156 1.451434 1.447811 1.445762 1.444856 1.444758
## [25] 1.445216 1.446047
## [1] 17
## [1] 2.369239 2.379584 2.390265 2.400941 2.411044 2.419675 2.425505 2.426720
## [9] 2.421118 2.406498 2.381435 2.346236 2.303499 2.257659 2.213547 2.174883
## [17] 2.143541 2.119738 2.102661 2.091076 2.083725 2.079514 2.077569 2.077223
## [25] 2.077984 2.079495
## [1] 1.653720 1.657376 1.660848 1.663907 1.666216 1.667291 1.666474 1.662926
## [9] 1.655691 1.643876 1.626982 1.605323 1.580295 1.554217 1.529682 1.508752
## [17] 1.492481 1.480943 1.473563 1.469500 1.467902 1.468044 1.469356 1.471420
## [25] 1.473936 1.476699
## [1] 18
## [1] 2.351354 2.359120 2.367655 2.376930 2.386826 2.397062 2.407113 2.416112
## [9] 2.422783 2.425500 2.422590 2.412915 2.396524 2.374934 2.350698 2.326502
## [17] 2.304382 2.285425 2.269912 2.257620 2.248095 2.240830 2.235356 2.231277
## [25] 2.228270 2.226086
## [1] 1.570366 1.574824 1.579491 1.584262 1.588953 1.593264 1.596736 1.598714
## [9] 1.598347 1.594682 1.586891 1.574643 1.558455 1.539757 1.520510 1.502577
## [17] 1.487211 1.474922 1.465640 1.458971 1.454411 1.451472 1.449740 1.448886
## [25] 1.448658 1.448871
## [1] 19
## [1] 2.403991 2.411406 2.419417 2.427939 2.436779 2.445567 2.453679 2.460156
## [9] 2.463676 2.462667 2.455663 2.441908 2.421932 2.397672 2.371891 2.347232
## [17] 2.325506 2.307526 2.293321 2.282481 2.274432 2.268593 2.264457 2.261611
## [25] 2.259731 2.258570
## [1] 1.596309 1.599761 1.603274 1.606723 1.609908 1.612523 1.614123 1.614110
## [9] 1.611749 1.606287 1.597175 1.584390 1.568677 1.551497 1.534612 1.519531
## [17] 1.507136 1.497661 1.490879 1.486340 1.483549 1.482057 1.481500 1.481596
## [25] 1.482139 1.482975
## [1] 20
## [1] 2.400439 2.407197 2.414341 2.421725 2.429076 2.435933 2.441576 2.444968
## [9] 2.444763 2.439493 2.427982 2.409955 2.386517 2.360060 2.333509 2.309348
## [17] 2.289041 2.273017 2.260995 2.252355 2.246399 2.242487 2.240092 2.238802
## [25] 2.238305 2.238371
## [1] 1.593953 1.598257 1.602700 1.607158 1.611428 1.615185 1.617950 1.619051
## [9] 1.617633 1.612754 1.603616 1.589932 1.572280 1.552170 1.531664 1.512722
## [17] 1.496660 1.483997 1.474626 1.468092 1.463824 1.461277 1.459992 1.459608
## [25] 1.459853 1.460525
